scholarly journals On the Generalized Transformation Functions

1949 ◽  
Vol 1 (1) ◽  
pp. 12-20 ◽  
Author(s):  
Yasutaka Tanikawa
2021 ◽  
Author(s):  
Maciej Migdal ◽  
Dan Fu Ruan ◽  
William F. Forrest ◽  
Amir Horowitz ◽  
Christian Hammer

Human immunogenetic variation in the form of HLA and KIR types has been shown to be strongly associated with a multitude of immune-related phenotypes. We present MiDAS, an R package enabling statistical association analysis and using immunogenetic data transformation functions for HLA amino acid fine mapping, analysis of HLA evolutionary divergence as well as HLA-KIR interactions. MiDAS closes the gap between inference of immunogenetic variation and its efficient utilization to make meaningful discoveries.


1990 ◽  
Vol 141 ◽  
pp. 111-114
Author(s):  
A. V. Voinov

The astonomical consequences of recently developed theoretical methods of relativistic astrometry are discussed. The set of practically important reference systems is described. These reference systems generalize the locally inertial frames of general relativistic test observer, the hierarchy of Jacoby coordinates for dynamical problems and the dynamically inertial reference systems of fundamental astrometry. In practical application of this formalism much attention is paid for relativistic transformation functions relating the ∗∗ecliptical coordinates corresponding to the baryecnters of the Solar system, the Earth-Moon subsystem and the Earth. Solutions to several kinds of relativistic precession are also presented.


Author(s):  
Maedeh Enayati ◽  
Omid Bozorg-Haddad ◽  
Javad Bazrafshan ◽  
Somayeh Hejabi ◽  
Xuefeng Chu

Abstract This study aims to conduct a thorough investigation to compare the abilities of QM techniques as a bias correction method for the raw outputs from GCM/RCM combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the CORDEX dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.


Author(s):  
Baoli Wang ◽  
Jiye Liang ◽  
Yuhua Qian ◽  
Chuangyin Dang

Decision makers often express their evaluations on decision problems with multi-granular linguistic terms. This fact leads to the unification of the multi-granular linguistic terms into a single linguistic set in the literature. However, this unification process increases the complexity of computation and the subjectivity in the determination of transformation functions. To overcome this deficiency, this paper aims to develop a normalized numerical scaling method for determining the semantics of multi-granular linguistic terms in the same domain. We first introduce a class of numerical scaling functions to generate several balanced or unbalanced linguistic sets. Since these scaled linguistic sets have different domains, we then develop a normalized numerical scaling method to form them into the unique interval [0,1]. As a result of this development, two classes of normalized scaling functions are derived from the priori scale information and applications of piecewise linear interpolation and piecewise arc interpolation. Finally, an example is given to illustrate how the method works.


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